Vibe Coding is Programming or Creation? No-Code? Programming with AI

The 2025/2027 programming market is booming. Learn about Vibe condig and get ready to enjoy the possibilities, learning about the scenarios, challenges, and opportunities.

What is Vibe Coding?

In simple terms, vibe coding is an AI-driven programming technique in which the user provides a description of the problem or functionality in natural language (prompt), and a large language model (LLM) generates the source code needed to implement the solution.

In this approach, the programmer does not write the code line by line; instead, he tests and refines the code generated by the AI ​​until he achieves the desired result. As Jhonata Emerick (CEO of Datarisk) explained, “vibe coding uses artificial intelligence to transform descriptions in natural language, such as Portuguese or English, into functional code”, with the programmer acting almost as a “creative director” who guides the generation of the software.

The term “vibe coding” was coined by computer scientist Andrej Karpathy (former director of AI at Tesla and co-founder of OpenAI) in February 2025. Karpathy described this new way of coding as conversational, in which he “gives in completely to the vibes” and even forgets that the code exists, just giving voice or text commands to the AI ​​to generate the program. According to him, “it’s not really programming – I just see things, say things, do things, copy and paste things, and it usually works.” This approach has only recently become possible due to the evolution of advanced generative models (such as ChatGPT, Claude, etc.), which can interpret ordinary language and produce executable code in seconds. In short, vibe coding realizes Karpathy’s vision that “the newest programming language is English” – that is, it becomes possible to program by describing in natural language instead of writing in the syntax of traditional languages. How it works in practice: Instead of dictating detailed logic, the developer provides high-level instructions about what the software should do, and the LLM returns code snippets that implement those instructions. The process tends to be iterative: the developer evaluates the generated code, runs it to test it, and then refines the specification or makes new prompts to fix bugs until a satisfactory solution is reached. Often, especially in simple projects or prototypes, the final code can be obtained without the programmer having to understand every line – he trusts that the AI ​​has worked out the internal details while he focuses on the desired result.

This characteristic distinguishes vibe coding from simply using AI as support: the vibe coder can accept ready-made code snippets without necessarily fully understanding their implementation, something possible but risky, as we will discuss later.

Tools such as chatgpt, gemini, github copilot, loveable, claude, help in the development of technological solutions, using live coding, AI programming, and even no-code scenarios.

If you want to explore this scenario with a good programming foundation and then delve deeper into whatever you deem necessary, you can start with the Java in Practice course, where you learn a programming language with a database and quickly get up to speed, so you can make the most of this scenario in a short space of time. Access it here: https://pay.kiwify.com.br/55BuhNc

Difference in relation to the “no-code” movement: Both aim to reduce the technical barrier to creating software, but they are different approaches. In no-code, visual interfaces (blocks, draggable components) are used to assemble simple applications without writing code; vibe coding involves generating real code via natural language, allowing for more personalized and complex solutions.

In other words, in vibe coding, the user describes what they want in free text and the AI ​​produces editable source code in languages ​​such as Java, Python, JavaScript, etc., offering more flexibility than the limitations of predefined templates in no-code.

Therefore, many see vibe coding as an evolution of no-code, combining the ease of high-level description with the power of real code generated by AI.